: The aims of this proposal are to develop statistical methods to make appropriate and efficient use of the data collected in epidemiologic and clinical studies in which a fraction of the subjects will not develop the endpoint. This is a frequent occurrence in cancer studies when some patients can be cured. Statistical models, called cure models, have been proposed to analyze such data. Methods for incorporating covariates into cure models will be developed. Both time fixed and time dependent covariates will be considered. The methodology will be applied to datasets from prostate cancer, head and neck cancer, breast cancer and osteosarcoma. An accelerated failure time regression cure model is proposed for datasets with time fixed covariates. An EM algorithm estimation method incorporating a non-parametric baseline latency distribution will be investigated. A method is proposed for estimating non-linear covariate effects in cure models, using penalised likelihood. Proper statistical modeling with time dependent covariates is much more difficult. In the second project, the applicants will investigate a method of joint modeling of failure times and time dependent (internal) covariates in cure models. The proposed model combines aspects of a mixture model for the cured and uncured groups, a random effects model for longitudinal data and a proportional hazards model for failure time data. They will develop a Gibbs sampling approach for this model. Application os this model to auxiliary variables in clinical trials will be given. The methodology proposed will allow time dependent auxiliary variables to provide additional information for censored cases, and thus give more accurate and efficient conclusions from the trial.